[nwpu-zxr]

Purpose

This PR adds initial decode-context-parallel(DCP) support for PD-disaggregation Mooncake connector.

The implementation targets the minimal supported DCP scope first:

• Supports MLA and GQA/full-attention KV cache transfer.
• Supports heterogeneous P/D DCP sizes, including DCP enabled on only one side.
• Supports P/D TP size differences when the KV head-group mapping is valid.
• Filters transferred block IDs by the intersection of producer/consumer DCP sequence-block ownership.
• Adds explicit runtime validation for currently unsupported DCP cases:
  • multiple KV cache groups / hybrid KV cache groups
  • Mamba/GDN KV cache groups
  • sliding-window attention
  • mismatched P/D block sizes

This PR does not yet add DCP with Mamba support. Those can be added in follow-up PRs.

Test Plan

• E2E basic validation on Ascend hardware:

  • Model: DeepSeek-V2-Lite
  • Validate Mooncake PD-disaggregation DCP transfer with multiple P/D DCP combinations:
    • Prefill DCP4 -> Decode DCP1
    • Prefill DCP4 -> Decode DCP2
    • Swapped P/D roles for the above combinations
  • Validate both short and long prompts(7-1770 tokens)

• Follow-up validation plan:

  • Run GPU backend validation with a GQA model, e.g. Qwen3.
  • Add full GPQA-Diamond dataset evaluation for both MLA and GQA models.
  • Cover additional heterogeneous TP/DCP combinations on GPU after the initial Ascend validation.

Test Result

• Ascend hardware e2e: DeepSeek-V2-Lite Mooncake PD-disaggregation with Prefill DCP4 -> Decode DCP1.
• Ascend hardware e2e: DeepSeek-V2-Lite Mooncake PD-disaggregation with Prefill DCP4 -> Decode DCP2.
• Ascend hardware e2e: DeepSeek-V2-Lite Mooncake PD-disaggregation with swapped P/D roles for DCP4/DCP1 and DCP4/DCP2 combinations.
• GPU backend e2e: GQA model validation with Qwen3.
• Benchmark/eval: full GPQA dataset evaluation for MLA models.
• Benchmark/eval: full GPQA dataset evaluation for GQA models.

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• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
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Rebase and DCP validation update (2026-10-19)

This updates the existing PR rather than opening another implementation. The scope remains single-group MLA/full-attention transfer with valid KV head-group mapping. Hybrid DCP work such as #52271 and #53730 is outside this PR's scope.

• Rebased onto upstream main at dde9aca41cd3ccceb1ffa902933b6678a09c83ce.
• Require equal P/D physical block sizes, cp_kv_cache_interleave_size == block_size, and no logical-to-kernel block splitting for DCP. P/D DCP sizes must divide one another. Enabled DCP must consume all redundant KV-cache replicas (MLA: DCP equals TP).
• FlashInfer MLA currently requires interleave 1 for DCP and is incompatible with this block-aligned transfer path; select an attention backend supporting block-aligned DCP. No FlashInfer kernel change is included.
• Extend the existing NIXL PD configuration conditions to Mooncake. No changes to parallel.py or kv_transfer_state.py.
• When producer DCP is disabled, all relevant producer replicas participate in completion accounting; one canonical replica sends the payload.
• No additional test cases were added during this update; existing fixtures and one replica-routing expectation were adjusted.

Validation and limitations

The new hardware checks used a separate Ascend adaptation of vLLM 0.30.0 with DeepSeek-V3.1-Terminus W4A8 QuaRot weights, 16K context, block size/interleave 128, and MTP disabled. The Ascend-only transfer-engine initialization, split nope/rope tuple registration, 2 MiB registration alignment, launch scripts and proxy workaround are not included in this PR.

The DCP routing, head-group mapping, block filtering, transfer plan, coalescing eligibility, interleave validation and receive-dispatch functions match the tested adaptation by AST comparison (10 functions). Current upstream layout caching and packed-region paths are preserved but were not directly exercised by that older runtime.

Hardware coverage:

• P/D DP1 TP8 DCP8; then EP-enabled P DP2 TP4 DCP4 -> D DP4 TP2 DCP2 and the reversed topology. P used eager mode; D used graph mode with AIV communication.
• Curl completion checks used prompt lengths around 128/256/512/1024/2048/4096 boundaries, temperature 0, seed 1024 and max_tokens 32, comparing PD against direct D.
• P DCP4 -> D DCP2: 38/38 responses included the expected answer, 22/38 matched direct-D tokens exactly.
• P DCP2 -> D DCP4 with the validation-only P ignore_eos workaround: 38/38 transfers completed, 36/38 responses included the expected answer, 30/38 matched direct-D tokens exactly. The original prompt could also produce first-token EOS on direct P; the root cause remains unresolved.
• A different Chinese retrieval prompt at 1025 and 2049 tokens produced the correct answer prefix in 16/16 PD requests per length, with no first-token EOS. Exact PD/direct-D token matches were 10/16 and 16/16 respectively.

These are limited transport/inference smoke checks, not a full numerical-accuracy pass or a GPU E2E result. GPU MLA/GQA E2E, the current upstream packed-layout path and full dataset evaluation remain pending. Earlier DeepSeek-V2-Lite results above are historical PR results, not rerun in this update.

Commands/checks for this update:

ruff check tests/v1/kv_connector/unit/test_mooncake_connector.py vllm/config/vllm.py vllm/distributed/kv_transfer/kv_connector/v1/mooncake/{mooncake_connector,mooncake_utils}.py
ruff format --check tests/v1/kv_connector/unit/test_mooncake_connector.py vllm/config/vllm.py vllm/distributed/kv_transfer/kv_connector/v1/mooncake/{mooncake_connector,mooncake_utils}.py
git diff --check
# Remote validation harnesses invoke curl /v1/completions with saved tokenized requests:
python /home/z00943888/dcp/diagnose_dcp_new_prompt.py

Ruff checks, formatting, four-file syntax compilation and the AST parity check passed. The applicable Python hooks also passed (mypy, typos, test tethering, SPDX, filenames, forbidden imports, torch.cuda calls and boolean context managers). These used the repository hook definitions filtered into a temporary config; full pre-commit initialization was interrupted while downloading an unrelated actionlint toolchain. GPU-dependent unit tests were not run locally.

AI assistance: Codex assisted with the rebase, code review, compatibility edits and validation analysis under the submitter's direction.

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @nwpu-zxr.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[wjinxu]

Hi @nwpu-zxr, thanks for this PR! Are you still working on it? I noticed it's been in draft with merge conflicts for a while. Did you run into any blockers? If possible, I'd love to join and help move it forward. Would that be okay with you?

[nwpu-zxr]

Hi @nwpu-zxr, thanks for this PR! Are you still working on it? I noticed it's been in draft with merge conflicts for a while. Did you run into any blockers? If possible, I'd love to join and help move it forward. Would that be okay with you?

Hi @wjinxu, I've rebased #53205 onto the latest upstream main. Thanks for offering to help!

The current scope and constraints are:

• Single KV-cache-group MLA or GQA/full-attention models; hybrid groups, Mamba/GDN, and sliding-window attention are not supported.
• P and D must use the same physical block size, with cp_kv_cache_interleave_size == block_size. Logical-to-kernel block splitting is not supported.
• Different P/D TP and DCP sizes are supported when the KV head-group mapping is valid and one DCP size divides the other.
• When DCP is enabled, it must cover all redundant KV-cache replicas; for MLA, this means DCP == TP.
• PCP support is outside this update.

I've also completed some basic inference correctness checks on Ascend hardware using DeepSeek-V3.1-Terminus, with MTP disabled. These covered DCP8→DCP8, DCP4→DCP2, and DCP2→DCP4, using short and long prompts around block boundaries, including 1025 and 2049 tokens, and comparing PD output against direct-D inference. The checks exercised KV transfer and answer correctness, but some outputs were not token-identical, so this is not yet a full accuracy validation.

GPU resources are currently limited on my side, so I haven’t been able to validate this on GPUs yet. I’d really appreciate any help with GPU testing or code review. I also plan to gradually extend the implementation to support multiple KV-cache groups, PCP, and other configurations. Thanks again for your interest and support!

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @nwpu-zxr.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork